Gradient-based meta-learning is an approach where models learn to adapt quickly to new tasks by optimizing their learning algorithms using gradient descent. This method enables the model to leverage prior knowledge to efficiently learn new tasks with minimal data by updating its parameters in a way that generalizes across tasks.

Gradient-based Meta-learning

Gradient-based meta-learning

models learn to adapt quickly

Optimizing learning algorithms

Gradient descent is an optimization algorithm used to minimize a function by iteratively moving towards the steepest descent, or negative gradient, of the function. It is widely used in machine learning to update model parameters and minimize the loss function, ensuring the model learns efficiently from data.

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